Inhwan Bae
·
Hae-Gon Jeon
AAAI 2023
Project Page
AAAI Paper
Source Code
Related Works
This repository contains the code for control point conditioned prediction and the initial trajectory refinement network for trajectory prediction.
Environment
All models were trained and tested on Ubuntu 18.04 with Python 3.7 and PyTorch 1.6.0 with CUDA 10.1.
Dataset
Preprocessed ETH and UCY datasets are included in this repository, under ./dataset/
.
The train/validation/test splits are the same as those fond in Social-GAN.
To train our Graph-TERN on the ETH and UCY datasets at once, we provide a bash script train.sh
for a simplified execution.
./scripts/train.sh
We provide additional arguments for experiments:
./scripts/train.sh -p <experiment_tag_prefix> -s <experiment_tag_suffix> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>
# Examples
./scripts/train.sh -d "hotel" -i "1"
./scripts/train.sh -p graph-tern_ -s _experiment -d "zara2" -i "2"
./scripts/train.sh -d "eth hotel univ zara1 zara2" -i "0 0 0 0 0"
If you want to train the model with custom hyper-parameters, use train.py
instead of the script file.
python train.py --input_size <input_coordinate_dimension> --output_size <output_gaussian_dimension> \
--n_epgcn <number_of_control_point_gcn_layers> --n_epcnn <number_of_control_point_cnn_layers> \
--n_trgcn <number_of_refinement_gcn_layers> --n_trcnn <number_of_refinement_cnn_layers> \
--n_ways <number_of_control_points> --n_smpl <number_of_samples_for_refine> --kernel_size <kernel_size>\
--obs_seq_len <observation_length> --pred_seq_len <prediction_length> --dataset <dataset_name> \
--batch_size <minibatch_size> --num_epochs <number_of_epochs> --clip_grad <gradient_clipping> \
--lr <learning_rate> --lr_sh_rate <number_of_steps_to_drop_lr> --use_lrschd <use_lr_scheduler> \
--tag <experiment_tag>
We have included pretrained models in the ./checkpoint/
folder.
To evaluate our Graph-TERN at once, we provide a bash script test.sh
for a simplified execution.
./scripts/test.sh -p <experiment_tag_prefix> -s <experiment_tag_suffix> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>
# Examples
./scripts/test.sh
./scripts/test.sh -d "hotel" -i "1"
./scripts/test.sh -p graph-tern_ -s _experiment -d "zara2" -i "2"
./scripts/test.sh -d "eth hotel univ zara1 zara2" -i "0 0 0 0 0"
If you want to evaluate the model individually, you can use test.py
with custom hyper-parameters.
python test.py --tag <experiment_tag> --n_samples <number_of_multimodal_samples>
# Examples
python test.py --tag graph-tern_eth_experiment
python test.py --tag graph-tern_hotel_experiment
python test.py --tag graph-tern_univ_experiment
python test.py --tag graph-tern_zara1_experiment
python test.py --tag graph-tern_zara2_experiment
If you find this code useful for your research, please cite our trajectory prediction papers :)
💬 LMTrajectory (CVPR'24) 🗨️
|
1️⃣ SingularTrajectory (CVPR'24) 1️⃣
|
🌌 EigenTrajectory (ICCV'23) 🌌
|
🚩 Graph‑TERN (AAAI'23) 🚩
|
🧑🤝🧑 GP‑Graph (ECCV'22) 🧑🤝🧑
|
🎲 NPSN (CVPR'22) 🎲
|
🧶 DMRGCN (AAAI'21) 🧶
@article{bae2023graphtern,
title={A Set of Control Points Conditioned Pedestrian Trajectory Prediction},
author={Bae, Inhwan and Jeon, Hae-Gon},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2023}
}